Sensor array for generating network learning populations using limited sample sizes

Abstract

A method for generating a training data set for machine learning includes disposing a first sample component in or about a sensing apparatus. The sensing apparatus includes a plurality of sensors, each sensor being disposed at a unique position and angle relative to the first sample component. The method captures a first sensor output of each sensor, thereby generating a first training data set including a first plurality of sensor outputs. The method then manipulates at least one of the first sample component and an environment within the sensing apparatus, and captures an additional sensor output of each sensor, thereby generating an additional training data set including an additional plurality of sensor outputs. The method then reiterates the step of manipulating the at least one of the first sample component and the environment within the sensing apparatus and capturing the additional sensor output of each sensor. Finally, the method merges each of the sensor outputs in the first training data set and each additional training data set, thereby generating a full machine learning training set.

Description

TECHNICAL FIELD[0001]The present disclosure relates generally to methods and apparatuses for generating labeled neural network training data sets (e.g. learning populations), and more specifically to a system and method for generating the same from a limited sample size.BACKGROUND[0002]Machine learning systems, such as those using neural networks, are trained by providing the neural network with a labeled data set (referred to as a learning population) including multiple files with each file including tags identifying features of the file. In one example a quality control system using a neural network can be trained by providing multiple images and / or sound files of a product being examined with each image or sound file being tagged as “good” or “bad”. Based on the training set, the neural network can determine features and elements of the images or sound files that indicate if the product is good (e.g. passes quality control) or bad (e.g. needs to be scrapped, reviewed, or reworked...

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